Lab 7 of unit 9 done

Lab7.ipynb

@@ -0,0 +1,219 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "421a578c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: sqlalchemy in /home/ricci-preto/anaconda3/lib/python3.13/site-packages (2.0.51)\n",
+      "Requirement already satisfied: pandas in /home/ricci-preto/anaconda3/lib/python3.13/site-packages (3.0.3)\n",
+      "Requirement already satisfied: greenlet>=1 in /home/ricci-preto/anaconda3/lib/python3.13/site-packages (from sqlalchemy) (3.3.2)\n",
+      "Requirement already satisfied: typing-extensions>=4.6.0 in /home/ricci-preto/anaconda3/lib/python3.13/site-packages (from sqlalchemy) (4.15.0)\n",
+      "Requirement already satisfied: numpy>=1.26.0 in /home/ricci-preto/anaconda3/lib/python3.13/site-packages (from pandas) (2.4.6)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in /home/ricci-preto/anaconda3/lib/python3.13/site-packages (from pandas) (2.9.0.post0)\n",
+      "Requirement already satisfied: six>=1.5 in /home/ricci-preto/anaconda3/lib/python3.13/site-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install sqlalchemy pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "940c0fd4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sqlalchemy import create_engine\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "from dotenv import load_dotenv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e125a11",
+   "metadata": {},
+   "source": [
+    "Establishing a Connection and Defining rentals_month"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "2698fc1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load_dotenv()\n",
+    "\n",
+    "user = os.getenv(\"MYSQL_USER\")\n",
+    "password = os.getenv(\"MYSQL_PASSWORD\")\n",
+    "\n",
+    "engine = create_engine(\n",
+    "    f\"mysql+pymysql://{user}:{password}@localhost/sakila\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ca289dd",
+   "metadata": {},
+   "source": [
+    "Defining rentals_month"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7fddaac3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rentals_month(engine, month, year):\n",
+    "    query = f\"\"\"\n",
+    "    SELECT *\n",
+    "    FROM rental\n",
+    "    WHERE MONTH(rental_date) = {month}\n",
+    "      AND YEAR(rental_date) = {year};\n",
+    "    \"\"\"\n",
+    "\n",
+    "    df = pd.read_sql(query, engine)\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9edc80ce",
+   "metadata": {},
+   "source": [
+    "Defining rental_count_month"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "eb22908e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rental_count_month(df, month, year):\n",
+    "    column_name = f\"rentals_{month:02d}_{year}\"\n",
+    "\n",
+    "    result = (\n",
+    "        df.groupby(\"customer_id\")\n",
+    "          .size()\n",
+    "          .reset_index(name=column_name)\n",
+    "    )\n",
+    "\n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8e8602f",
+   "metadata": {},
+   "source": [
+    "Defining compare_rentals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "0bde660c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compare_rentals(df1, df2):\n",
+    "    merged = pd.merge(df1, df2, on=\"customer_id\", how=\"inner\")\n",
+    "\n",
+    "    rental_cols = [col for col in merged.columns if col != \"customer_id\"]\n",
+    "\n",
+    "    merged[\"difference\"] = (\n",
+    "        merged[rental_cols[1]] - merged[rental_cols[0]]\n",
+    "    )\n",
+    "\n",
+    "    return merged"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d467ba64",
+   "metadata": {},
+   "source": [
+    "Example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "19831ca2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   customer_id  rentals_05_2005  rentals_06_2005  difference\n",
+      "0            1                2                7           5\n",
+      "1            2                1                1           0\n",
+      "2            3                2                4           2\n",
+      "3            5                3                5           2\n",
+      "4            6                3                4           1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get rentals\n",
+    "may_rentals = rentals_month(engine, 5, 2005)\n",
+    "june_rentals = rentals_month(engine, 6, 2005)\n",
+    "\n",
+    "# Count rentals\n",
+    "may_counts = rental_count_month(may_rentals, 5, 2005)\n",
+    "june_counts = rental_count_month(june_rentals, 6, 2005)\n",
+    "\n",
+    "# Compare\n",
+    "comparison = compare_rentals(may_counts, june_counts)\n",
+    "\n",
+    "print(comparison.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1fd7c02d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}